Learning Lab Lower Saxony, Expo Plaza 1, D-30539 Hannover

Michael Sintek

Abstract

Abstract: Selecting appropriate learning services for a learner from the large number of heterogeneous knowledge sources is a complex and challenging task. This paper presents the idea of Smart Spaces for Learning. A Smart Space for Learning is defined as a distributed system (ie "space") that provides management support for the "smart" retrieval and consumption of heterogeneous learning services via Personal Learning Assistants. Personalisation and system interoperability play an important role for the realisation of a Smart Space for Learning. In this paper we illustrate and discuss how Semantic Web technologies such as RDF, TRIPLE, QEL and ontologies can be applied to create a Smart Space for Learning.

Abstract: Selecting appropriate learning services for a
learner from the large number of heterogeneous knowledge sources is
a complex and challenging task. This paper presents the idea of
Smart Spaces for Learning. A Smart Space for Learning is defined as
a distributed system (ie "space") that provides management support
for the "smart" retrieval and consumption of heterogeneous learning
services via Personal Learning Assistants. Personalisation and
system interoperability play an important role for the realisation
of a Smart Space for Learning. In this paper we illustrate and
discuss how Semantic Web technologies such as RDF, TRIPLE, QEL and
ontologies can be applied to create a Smart Space for Learning.

1. The Corporate Learning Spaces Today

Over the past few years, corporations have made significant
progress in linking learning processes with the employee's work
environment. Today's knowledge workers are served by Internet
access through their desktop and mobile phone, business-unit
specific knowledge repositories, e-learning tools, and customized
education and training opportunities available through corporate
intranets. Leading business organisations are offering its
workforce a heterogeneous set of learning resources ranging from
traditional seminars to knowledge management activities and
e-learning content.

While such a sophisticated learning space creates competitive
advantage by intellectually empowering a company's workforce, some
shortcomings limit the benefits, mainly from the perspectives of
decision effectiveness, process administration, and IT
infrastructure management. The lack of interoperability of
knowledge repositories, for instance, does not allow for a unique
view on the learning services offered. As a result, a user's search
costs increase and the transparency of learning resources offered
is reduced with each repository added to the environment. However,
such an environment not only lacks transparency in terms of
learning service offerings, but also does not provide a
customizable view of the learning processes undertaken by the work
force. The latter constitutes important information for personnel
developers and other mentors. In many cases, the electronic
environments also lack decision and recommendation support. Neither
potential learners nor their mentors have all the goal-driven
business tools and information available to concisely select the
right learning service for closing a particular knowledge gap. On
the other hand, a series of wrong decisions (eg not taking a
"required" learning service or registering for a "wrong" learning
service) can have substantial impact on individual and corporate
performance.

Until recently, setting up a corporate learning space consisting
of monolithic components such as traditional course offerings,
e-learning content (where appropriate), and knowledge management
activities has been a major task in corporate work environments.
However, this no longer seems to be the main concern. Companies are
starting to focus on the integrated management of these
heterogeneous components in what can be referred to as "Smart
Spaces for Learning". Besides the integrate view on a company's
human resources (HR) development process, institutions are now also
selectively opening up there knowledge environments to incorporate
also resources from other environments (eg book abstracts, courses
offered through electronic market places, etc).

In Smart Spaces for Learning, semantic web technologies are used
to provide enhanced, customizable and automated learning and
administrative services. These include technologies such as the
Resource Description Framework (RDF), the Query Exchange Language
(QEL), TRIPLE, and ontologies that play a crucial role in achieving
interoperability among repositories or recommending appropriate
learning services. This paper reports on the ELENA project[1] and investigates and
discusses how these technologies can be used to build systems like
Smart Spaces for Learning. Smart Spaces for Learning are defined in
Section 2, while Section 3 describes relevant design issues.
Sections 4 and 5 respectively address two of the design issues
mentioned: artefacts interoperability and personalisation. The
paper concludes with a presentation of the ELENA Smart Space for
Learning and discusses implications for the development of an
Educational Semantic Web.

2. "Smart Spaces for Learning" Defined

A Smart Space for Learning is a distributed system, which
provides management support for the retrieval and consumption of
heterogeneous learning resources. While "Space" is used as a
synonym for "Network", "Smart" refers to the 'intelligent'
mediation of learning resources (eg courses, e-learning content,
etc) based on user profiles and artificial intelligence
techniques.

Like any information system also a Smart Space for Learning
consists of a human component and a technology component. Smart
Spaces for Learning are built for supporting human resources
development processes. Hence, learners, educators (eg teachers,
instructors, trainers, professors, peers), and learning managers
(eg parents, HR developers, team leaders) constitute the primary
users of the system.

The two major technology components of Smart Spaces for Learning
are the network of interconnected educational nodes (the Learning
Management Network) and a Personal Learning Assistant (PLA), which
provides a personalised access point to learning resources on the
network (see Figure 1). The PLA supports learners in searching for,
selecting, contracting with, and evaluating learning resources. It
might also assess the learner's pre-existing knowledge to better
identify knowledge gaps and learning needs. By using
personalisation techniques a PLA is capable of creating a
personalised view of a Learning Management Network.

Figure 1: System components of a Smart Space for
Learning

In a Learning Management Network, system interfaces provide
means for exchanging information on educational artefacts such as
courses, offer information and learner profiles. The information on
educational artefacts (ie data on data) is commonly referred to as
metadata and plays a crucial role for achieving interoperability
among the various educational nodes. A Learning Management Network
is a "trusted" network in which users and systems are
authenticated.

We envision learning management networks as sub-networks of a
larger Educational Semantic Web -- according to ELENA terminology
also referred to as Artefacts and Service Network. The Educational
Semantic Web facilitates the identification of educational nodes,
both, in terms of network location as well as service types
offered. The types of services offered comprise learning services
and services that supplement learning services, which facilitate
the preparation, generation, control, or evaluation of learning
services. For example, a content brokerage service can be used for
preparing the delivery of a course or for providing a learner with
related information in a particular subject area. Assessment
services can be used to identify knowledge gaps. Evaluation
services provide information that helps to gauge the quality of a
learning service. Reputation services attempt to quantify the
reputation of a learning service provider within the network.
Designers of Learning Management Networks can take advantage of the
variety of educational services offered in the Educational Semantic
Web by integrating external educational nodes into their Smart
Space for Learning.

3. Design Issues

The implementation of Smart Spaces for Learning creates a
variety of design challenges including the following:

Network Design: Here, issues such as how can a network
be set up that provides a flexible framework for the registration
of educational nodes need to be addressed. Additionally, the
network needs to support a communication framework for exchanging
messages between the various educational nodes.

Interoperability of Educational Nodes: Within a smart
space for learning, common interfaces need to be created to make
educational nodes interoperable (Simon, Retalis, & Brantner, 2003). Basic
specifications or standards for exchanging information on
educational artefacts and triggering the delivery of learning
services and resources need to be defined.

Artefacts Interoperability: Educational artefacts are
understood as descriptions of educational service types (eg a
course catalogue or an evaluation service) or instances of
educational services and resources (eg a particular course, an
assessment activity or an online text book). When an educational
node forwards an educational artefact to another educational node
for further processing, both nodes need to speak a common language.
Hence, an ontology needs to be designed to provide a lingua
franca -- common trade language for learning resources -- in
the Smart Space for Learning.

Personalisation: When a Smart Space for Learning
provides access to a vast number of learning resources and services
the problem arises of how to find appropriate learning services
which satisfy a learner's demand. To solve that problem,
intelligent PLAs need to handle learner profiles (Dolog &
Nejdl, 2003a) and utilize them to recommend learning services
(Dolog & Nejdl, 2003b) and learning paths
according to their needs.

Support of Human Resources Development Processes: With
the implementation of a PLA, organizations aim at improving the
effectiveness of learning service selection decisions. Hence, the
PLA shall support various management techniques that can be
combined as a powerful tool supporting the effective selection of
learning services and optimising the transfer of knowledge
according to corporate goals.

Privacy and Security: Privacy is a major concern when it
comes to the design of a Smart Space for Learning. Learners
submitting a personalised search request need to be able to control
the information they are willing to submit to the learning
management network.

In the following sections, we focus on how Semantic Web
technologies such as TRIPLE, RDF, QEL, and ontologies can be used
to achieve artefacts interoperability and personalisation. Other
design issues are not addressed.

4. Artefacts Interoperability

In a Smart Space for Learning several educational nodes that use
different schemas for describing educational artefacts need to
communicate with each other. A possible approach to tackling the
problem of artefacts interoperability is to create pair wise
mappings (Aberer, Cudré-Mauroux, &
Hauswirth, 2003). This approach is based on an idea that the
schema of each system connected maintains mappings to the schemas
of "neighbouring" systems.

However, this might require a large number of mappings in case
many systems need to be interconnected. Another approach is to use
one shared ontology in a particular community as a mediating schema
and all local schemata in that community used by the systems
interconnected are mapped to this common schema. In other terms, an
ontology is terminology consisting of a set of related/associated
concepts (Gruber, 1993) that are shared by software such as a
Personal Learning Assistant. These concepts are used to describe
information in the application domain in a way suitable for machine
processing. We recognize two kinds of ontologies. One kind is used
to prescribe structures for information about educational
artefacts. Another kind is used to prescribe value ranges of
particular properties in former ontologies as controlled
taxonomies/vocabularies (eg subject ontologies).

The ELENA Smart Space for Learning is also built upon a common
ontology describing the educational artefacts subject to exchange.
Identifying learning services as special instances of learning
resources is for example an important design assumption of the
ELENA ontology. In ELENA we assume that learning resources, similar
to learning objects as defined by the IEEE Learning Object Metadata
(LOM) Standard (IEEE, 2002) can be seen as any kind of (digital
and non-digital) material or person, which facilitates the delivery
of learning. Learning materials such as textbooks, lecture notes,
computer-based training applications, etc, as well as educators are
examples of learning resources. A learning service is defined as an
event that is provided by a learning service provider in order to
support the accomplishment of a specific learning objective. This
is achieved by creating a learning environment consisting of
learning resources, communication devices, meeting places, etc.
Learning services are primarily concerned with various functions of
instruction, such as motivating learners, re-calling learners'
pre-existing knowledge, conveying learning content, providing
exercises, and learner assessment. They are frequently identified
with a specific type of outcome (eg grade, certificate, degree,
etc) and sometimes require specific prerequisites to be fulfilled
before a learner is allowed or recommended to interact with the
service.

Since learning services require also many other learning
resources, they are usually quite costly. In a corporate setting
also opportunity costs have to be taken into account in addition to
course price and accommodation costs. On the other hand, learning
material is often freely available on the Internet. In some cases
the provision of learning material is combined with a usage license
(Quemada & Simon, 2003), so called open content
licenses, while sometimes a specific price as to be paid which is
usually significantly smaller than the price of a similar learning
service.

Educational nodes aiming to share artefacts in a Smart Space for
Learning then need to map the local schema to the common ontology.
In this section we aim to illustrate what such a mapping can look
like. We take the case of a schema developed for the ULI
(Universitärer Lehrverbund Informatik) project (ULI, 2001). In
ULI courses are described according to the schema presented in
Figure 2.

The main concepts used to describe ULI courses are
Resource, Course, and Module. All these
concepts are described using the same attributes: creator,
created, subject, language,
description, hasPart, title, and
requires. Prefixes used within the attributes refer to
abbreviations of schemas URIs which define the attributes in IEEE
LOM RDF bindings (eg dc refers to Dublin Core). The isa
relations between the Resource, the Module, and the
Course indicate that the attributes are inherited from
Resource. In addition, Course and Module can
have additional attributes like time, location and so on. The main
concepts used also refer to other classes such as W3CDTF
(W3C Date Time Format) for describing date of creation.
W3CDTF class prescribes a structure of date, time, format.
It contains properties for day, month, year, time zone, hour,
minute, and fraction of second. LOM schema allows by
lom:entity to reference the vCard (Dawson
& Howes, 1998) standard for describing persons. In this
case it is used for representing a person who created a particular
course, module or resource. FN is another concept
prescribing a structure for full name of the person who created the
course, module or resource. A course can have a composite
structure. Hence, a course can be composed from other courses,
modules and resources (hasPart* relation together with
hasPart attribute). Instances of the courses, modules or resources
are maintained in the hasPart relation.

In ELENA, we have developed a common ontology as a shared
conceptualisation. The ontology was created reusing concepts from
the IMS Learning Design specification (IMS, 2003) with some specifics
required for ELENA. Figure 3 depicts a basic set of concepts used
within LearningService. The LearningService class is
a subclass of the LearningResource class. There are other
subclasses of the Learning Resource which are not depicted in the
figure. The LearningService can have a
LearningObjective, and can create a Certification if
the LearningObjective issuccessfully achieved by the
learner.

LearningMaterial is a subclass of
LearningResource. Tutorial, LectureNote and
Example are possible subclasses of LearningMaterial.
This ontology is described with the TRIPLE model @elenaont and
uliont references the ULI schema (http://triple.semanticweb.org/
provides an introduction into TRIPLE).

Mapping will help us to achieve a subpart relation between the
schemas mentioned. To achieve the interoperability or the
possibility of querying ULI schema using ELENA Learning Service
ontology), some concepts from the ULI schema have to be aligned by
mappings. Our assumption in this context is that Course and
Module can serve as learning services. The simple mapping
rule in TRIPLE reflecting that assumption is:

Using these rules we can create a parameterized model in TRIPLE
which allows users to query the ULI resources only in terms of the
ELENA ontology. The following rules map Course in ULI to
Course in ELENA and Modules in ULI to Lectures
in ELENA.

There are other rules we use to map ULI schema to the ELENA
ontology, eg to derive environments used in ULI, to classify
resources in ULI, to derive Prerequisites and Learning
objectives in ULI, and so on. You can find more complete
example on using TRIPLE views for mappings between ontologies in
Miklós, Neumann, Zdun, & Sintek,
2003.

Using these rules we can create a parameterized model in TRIPLE
which allows users to query the ULI resources only in terms of the
ELENA ontology. The following query is an example for such a query,
and returns all Courses -- where the course is meant in the
context of the ELENA ontology -- while the answer was originally
described with the ULI ontology:

After applying the rules on ULI we can reuse the personalisation
services, eg recommendation, query rewriting or other services
provided in the ELENA network which use the ELENA ontology as a
communication language to deal also with ULI Resources and
Courses provide in the ELENA network.

5. Personalisation

Personalisation in a Smart Space for Learning can be based on
metadata about learners and metadata about learning resources. By
matching a learner profile with the descriptions of the resources
available, a personalised view on a Learning Management Network can
be provided. The matching process is performed by using inference
rules, which determine whether a service or resource is recommended
or filtered. Inferring can also be used to identify related
resources or to create a suitable learning path (Dolog, Gavriloaie, Nejdl, & Brase, 2003).

5.1. Representing Learner Profiles

In recent years there have been some efforts to standardise
learner profiles. The two most important initiatives in this
context are the IEEE Personal and Private Information (PAPI)
(IEEE,
2000) and IMS Learner Information Package (LIP) (IMS, 2001).
Concepts introduced by these initiatives can be used to personalise
a learner's view in a learning management network.

IEEE PAPI, for example, provides a comprehensive and well
developed structure for managing a learner's learning
performance. Besides other information, one can store
competencies gained in that structure. The competency or concepts
learned were usually acquired during the consumption of a learning
service or a resource. This information can be stored in such a
structure as well. In addition, the competency level of a
particular topic can be maintained using that structure. An example
of the performance category using a TRIPLE representation of RDF is
shown below.

The example depicts a performance record of a learner
"student1". He knows about Skolem Functions at the level of
0.6. This level of knowledge has been derived from an appropriate
annotation for the (already read) Praedikatenlogik3.pdf
resource and evaluated by the test
Test_Praedikatenlogik3.pdf. For the topic we use the
competence field from the PAPI profile. To indicate the level of
knowledge, we use granularity (ie we measure the level of
knowledge for each topic), performance coding (in numbers),
performance metric (from 0 to 1) and performance
value (0.6). We also use bucket to specify the time,
which was required for performing the test.

Preferences of a learner can, for example, be split into
those for language, communication devices, location and concepts.
The IMS LIP accessibility category has four main parts: language,
preference, eligibility, and disability. The attributes of all four
parts can be unified by using a type ontology. Then, the language
preferences can for example have a language type or the
communication devices can have a device preference type and so on.
The preferences of learners can be used to recommend learning
services and resources constrained with a certain type and value of
the preference (language, device type used for delivery, etc) or to
restrict a query with the values from preference records.

A learner's role and aspirations within a company is also
very important information that can be used to help recommend and
customise learning services. The information can be combined with
the learner's career goals and his business objectives. The basic
scenario in the corporate environment can be to extend competencies
of learners at certain positions to satisfy needs to expand in a
particular area. This might include acquiring knowledge about new
selling strategies, new competencies in new technologies, etc.

5.2. Representing Learning Resources

Personalised access means that resources are recommended based
on some relevant aspects of the user. Which aspects of the user are
important or not depends on the personalization domain. For
educational scenarios it is important to take into account aspects
such as the level of expertise of the learner in a specific field,
whether she wants to obtain a certain qualification, has specific
language preferences, etc. Learner Preferences can be easily
exploited, especially when they coincide directly with the metadata
and metadata values used for describing a learning service or
resource. Some specific examples are provided below.

One can, for example, employ an approach where the subject value
of the learning service description is a URI pointing to a subject,
topic or competence ontology. This allows for the identification of
the subject that this learning resource deals with. The
classification scheme can be encoded by using classification
category and taxon feature of RDF bindings of the LOM RDF
Binding Guide (Draft Version) (Nilsson, 2001). Examples of
subject ontologies are the ACM computing classification system
(ACM,
1998) or eclass (ECLASS, 2003). The latter provides a service
classification for the education and training industry under the
subclass 25-25.

IEEE LOM (IEEE, 2002] allows us to describe also learning
service prerequisites in terms of either topic; other learning
resources; competencies; or certificates. The RDF bindings of LOM
uses requires concept for these purposes.

The prerequisites can be seen as constraints which determine
what competencies a certificate learner should have to be eligible
to participate in a service which has the prerequisites in its
metadata. This is another example of using information for
constraining resources in the ELENA network.

LOM provides the classification category with the purpose
element. The purpose element has several sub-elements:
prerequisite, educational, objective,
accessibility restrictions, educational level,
skill level, security level, or competency.
The accessibility restriction sub-element can be used to
define constraints for accessing the learning object or service
(see below). All required learner profiles to partake of such a
learning service can be encoded into the accessibility
restrictions.

Directly using the user model fields (PAPI) allows us to
directly search for resources, which conform to the user profile.
For example, the resource with the restricted access specified in
the previous example is intended for a user whose level of
knowledge about the skolem functions topic from ACM CCS is
greater than 0.5.

5.3. Query Transformation based on Learner Profiles

At an educational node, a query for learning services submitted
via a user interface is first translated into a formal query
language, for example SQL. This formal query can then be rewritten
using information stored in a learner profile using, for example,
TRIPLE. Such a rewritten query contains additional restrictions on
resources and services matching the query. The following example
demonstrates how such transformations can be implemented. Consider
for example a query in the Query Exchange Language (QEL) (Nilsson
& Siberski, 2003) and represented in TRIPLE as depicted
below.

The query looks for resources which describe a competence on
"Intelligence Agents". This is represented by the identifier
"I.2.11.1" in "dc:subject". The identifier points to the entry in
ontology available at the URI abbreviated by "acmcss". The
identifiers for ontology entries are prescribed by the ACM Computer
Classification System (ACM, 1998)

// original QEL query in TRIPLE

@edu:q1 {

edu:X[rdf:type -> edu:Variable;

rdfs:label -> "X"].

edu:st0[rdf:type -> edu:RDFReifiedStatement;

rdf:subject -> edu:X;

rdf:predicate -> dc:subject;

rdf:object -> acmcss:'I.2.11.1'].

edu:genQuery[rdf:type -> edu:QEL3Query;

edu:hasQueryLiteral -> edu:st0;

edu:hasResultType -> edu:TupleResult].

}

The rules which can add restrictions to the QEL query are
depicted below. The rules are created according to learner's
preference. The first rule in the personal preferences indicates
that the learner is interested in query results in German. With the
second type of rules a learner can express related interests, if he
issues a query to find learning services in a specific area,
additional query conditions will be added. In this case, when the
original user query contains a restriction on "Intelligent Agents",
the rules will generate additional restrictions on "dc:subject" to
the query with identifiers "I.2.11" and "I.2" respectively. Other
preferences of this type can easily be added to the personal
learning profile.

// user profile

@edu:p1 {

// we want only German resources, so add "dc:lang ->
lang:de"

edu:add1[rdf:type -> edu:AddSimpleRestriction;

rdf:predicate -> dc:lang;

rdf:object -> lang:de].

// add topic restriction:

// "if topic is restricted to I.2.11.1, add additional

// topic restrictions I.3.12.2 and I.4.13.3"

edu:add2[rdf:type -> edu:AddTopicRestriction;

edu:topic -> acmcss:'I.2.11.1';

edu:topic -> acmcss:'I.2.11';

edu:addTopic -> acmcss:'I.2'].

Based on this user profile the following modified query is
derived:

ns001:genQuery[rdf:type -> edu:QEL3Query;

edu:hasResultType -> edu:TupleResult;

edu:hasQueryLiteral -> ns001:st0;

edu:hasQueryLiteral -> edu:genid0;

edu:hasQueryLiteral -> edu:genid1;

edu:hasQueryLiteral -> edu:genid2].

edu:genid0[rdf:type -> edu:RDFReifiedStatement;

rdf:subject -> ns001:X;

rdf:predicate -> dc:language;

rdf:object -> ulang:de].

edu:genid2[rdf:type -> edu:RDFReifiedStatement;

rdf:subject -> ns001:X;

rdf:predicate -> dc:subject;

rdf:object -> acmcss:'I.2.11'].

ns001:X[rdf:type -> edu:Variable;

rdfs:label -> "X"].

ns001:st0[rdf:type -> edu:RDFReifiedStatement;

rdf:object -> acmcss:'I.2.11.1';

rdf:predicate -> dc:subject;

rdf:subject -> ns001:X].

edu:genid1[rdf:type -> edu:RDFReifiedStatement;

rdf:subject -> ns001:X;

rdf:predicate -> dc:subject;

rdf:object -> acmcss:'I.2'].

The rewritten query depicted above will now look for resources
and services which are annotated also with the more general ACM
categories "Distributed artificial intelligence" and "Artificial
intelligence". The additional concepts are referenced using the ACM
Computer Classification System identifiers "I.2.11" and "I.2" for
the categories mentioned above. The query will also specifically
look for results in German.

5.4 Personalization on Query Results

Recommendation and filtering based on the level of competence
acquired is one example of personalisation which can be performed
on the query results. The competence level is maintained in the
performance category of the learner profile. With this as a
starting point several different rules can be used to derive
recommendations. We can, for example, assume that a resource is
recommended when for all prerequisites of all covered concepts have
at least one performance record can be found in the learner
profile.

The rule can be realised in TRIPLE as follows:

FORALL U, S recommended (U, S) <-

learner(U) AND service(S) AND

FORALL Sl (prereq(S, Sl) ->

(FORALL C (concept(Sl, C) -> (EXISTS P

(U[papi:has->P]@uli:learner AND
performance(P) AND

P[papi:learning_competency ->
C]@uli:learner))))).

Other rules are needed to define for example what is a service
(service(S)) or who is a learner (learner(U)) and so on. In well
defined metadata we can assume that resources are classified using
types (e.g. LearningService) from ontologies. These types can then
be used to check for appropriate resources within predicates like
service(S) or learner(U). If these types classification are not
available heuristics can be used (e.g. service is everything which
is described by attributes from a certain schema). The rules can
conclude not only with information that a service or resource is
recommended.

6. Prototyping Smart Spaces for Learning

6.1. The ELENA Smart Space for Learning

Within the ELENA project a prototypical Smart Space for Learning
has been realized by September 2003. The prototype builds upon
EDUTELLA (Nejdl et al., 2002), a schema-based P2P networking
infrastructure using RDF and the JXTA Framework (Sun, 2003).
EDUTELLA provides a search service where a node is able to submit a
query to the network specifying supported metadata schemas. This
query is expressed in the QEL language (Nilsson
& Siberski, 2003), a query language based on Datalog, and
forwarded to the nodes with related content in the network. The
results of the query are sent back to the requester in the form of
RDF statements.

Since the educational nodes do not use the same kind of metadata
schema our network provides several integration possibilities to
them in order to facilitate the task. EDUTELLA adopts an approach
based on wrappers. A wrapper can be defined as a mediation
application. In our context, a wrapper is in charge of translating
between the mediating language used by EDUTELLA (QEL) and a
specific repository language (eg SQL). Currently different wrappers
are adapted to different kinds of repositories like relational
databases, RDF repositories, concept databases or file based
sources.

EDUTELLA is used for connecting educational nodes such as ULI
and Educanext. Clix, Arel, and ITeachYou connect to the Learning
Management Network via the Educanext portal. Figure 5 depicts the
current implementation of the ELENA network and the different
educational nodes already integrated into it:

· Educanext: Educanext is a web-based
platform which supports the creation and sharing of knowledge
(http://www.educanext.org/). The portal is based on the Universal
Brokerage Platform (UBP), which enables collaboration among
educators by providing a full range of services to support the
exchange of Learning Resources (Law, Maillet, Quemada, & Simon, 2003).

· ULI: The ULI (Universitärer
Lehrverbund Informatik) project, a University teaching network,
tries to establish an exchange of course material, courses and
certificates in the area of computer science (see also Section 4).
Eleven German universities with eighteen different professors have
agreed to exchange their courses and to allow students from one
university to attend courses at another university, using advanced
e-learning technologies (ULI, 2001). Figure 5 shows file based providers
for three concrete courses. Other courses are omitted for space
limitation and are represented by file based provider marked with
"...". However, each of the courses is provided via its own
file-based provider in the network.

· IMC CLIX: CLIX is a standard Learning
Management System (LMS) developed by the German software vendor
IMC. Like any other LMS, CLIX supports the administration of
learning services. CLIX stands for Corporate Learning and
Information Exchange.

· IteachYou: ITeachYou is an independent
multimedia learning environment, which is designed for use in the
internet or intranets. It can be considered as a presentation
template for the library of highly structured content in field of
information technology.

· Arel: Arel offers a unique training
solution for corporations, distance learning institutions and large
organizations. The Arel system enables experts to deliver live and
on-demand interactive broadcast sessions from a centre to a large
number of participants in virtual class sites and so called
"spotlight desktops".

The ELENA PLA provides a personalised search service, which
implements the rule-based personalization approach for query
transformation as described in Section 5. Figure 6 depicts the
PLA's user interface for formulating a query for a particular
concept or competence a user would like to acquire. Users can type
the concept or concepts into three provided fields or can select
the concepts from an ontology provided. The PLA integrates
recommendation, query transformation, ontology mappings and other
functionalities provided as web services. The details about the
integration/orchestration of services by PLA can be found in
Dolog, Henze, Nejdl, & Sintek, 2004.

The Personal Learning Assistant then creates an EDUTELLA QEL
query. The query is extended with restrictions by query rewriting
using preferences of the learner profile. Then the query is
submitted to the EDUTELLA network. After receiving results, the
Personal Learning Assistant takes advantage of a recommendation
service to filter the results. For example, a learning resource or
service is only recommended if all its prerequisite concepts are
understood. It is not recommended when no prerequisite concepts are
understood. If some prerequisite concepts are understood, a
document is partially recommended.

Figure 6 depicts a user interface for personalised search
results. As you can see, we use a traffic light metaphor to
annotate resources with recommendation information. A green light
marks the recommended resources, a red light is shown next to not
recommended resources and a yellow light stands for a partial
recommendation. The personal recommendation is depicted in the
first column (PReco). There is a second column (Reco), which
provides learners with a group-based recommendation. The
group-based recommendation is calculated according to
recommendations of learners from the same group.

6.2. Evaluation and Outlook

The implementation of our research prototype has helped us to
identify a number of open research questions when it comes to the
realization of the Educational Semantic Web in general, and the
implementation of Smart Spaces for Learning in particular. Below
the identified issues are presented according to the structure of
Section 3:

Interoperability of Educational Nodes: In order to
achieve service interoperability we have used a semi-automated
provision interface as well as query-interface directly connecting
to a predefined database table in our prototype. The experience
gained so far suggests that an interface fulfilling the following
requirements is needed:

The interface needs to abstract from authentication and access
control mechanisms. Learning Management Networks can be based on
different authentication mechanisms. Once authentication is
established the similar query methods shall be used.

The query interface needs to provide means for communicating
target schemas, so that an educational node can map the query
results accordingly.

With the Simple Query Interface (Simon, Duval, & Van Asche, 2004) an
international group of researchers on educational technology aims
to contribute a specification that meets these requirements.

Artefacts Interoperability: We have observed that
authors of educational artefact descriptions rather do metatagging
only from a local perspective. While some aspects are general
enough to be considered for any context, some aspects like
competencies covered, prerequisites and others are heavily context
dependent. To abstract from the context often requires an
additional (co-ordination) effort many metadata authors are not
willing to go through.
Metadata authors are in general reluctant to input data for a
complex metadata structure because it requires a significant
effort. This is especially an issue when it comes to "small"
educational artefacts with low value. It means that in open systems
you can find metadata without prerequisites or all subjects covered
and so on. This makes the re-use of this metadata difficult because
no assumptions can be made on the usage of specific concepts. This
category of problems can be labelled as an "incomplete metadata
problem" or a "quality of metadata problem". Investigators
researching these types of problems should focus on the heuristics
of how to find information which is not exposed by metadata.
Developers should work on developing metadata authoring tools,
which are capable of deriving metadata directly from the content.
The quality of the metadata has profound implications on the
precision of search and personalisation capabilities.

Personalisation: We described some steps towards such as
"rule-based" personalisation methods based on semantic web
description formats, subject ontologies and the logical layer of
the semantic web tower represented by TRIPLE reasoning, querying,
and transformation language for the semantic web. This area however
still requires further study and research.

Another problem connected with personalisation is the state of the
art of learner profile standards and learner models for open
systems. We have mentioned some features of a learner profile that
we use for personalisation. However, a commonly agreed
representation of learner profiles is still missing.

Last but not least, advanced personalisation methods have not
gained high acceptance in current industry practice. There are
personalisation approaches implemented in Google or Amazon. These
approaches, however, should be improved and adapted for learning
services.

Support of Human Resources Development Processes: While
a lot of investigations are carried out on issues such as how to
deliver courses effectively on-line, little research on how
learning management and training control can be supported using
information technology does exist. At the same time, new business
standards such as ISO9000 (2000) or Basel II stress the importance
of a well-managed corporate learning space. As a result learning
processes in companies have to become more effective. The ELENA
project is recently released a study focusing on the requirements
on the IT support of corporate HR development process (see
({Gunnarsdóttir, 2004 #616}). However, additional
investigations need to be carried out in order to design systems
that are able to learn from successful cases and apply critical
success factors (semi-)automatically in future scenarios.

7. Conclusion

From prototyping Smart Spaces for Learning we have identified
the following challenges for the evolution of the Educational
Semantic Web.

First, Interoperability is a major issue that needs to be
resolved. In order to make learning resources and educational nodes
interoperable a comprehensive educational ontology covering all
important aspects of learning management and learning delivery
would be beneficial. Our little experiments have already shown that
existing standards in that field such as IEEE LOM or IMS Learning
Design are not expressive enough to serve the needs of designers of
the Educational Semantic Web. At the same time tool support is
required in order to map local learning resource description with
the centralized-maintained. The tools need to become an
instructional environment by themselves in order to teach
annotators the concepts introduced by the ontology. Mapping tools
and services are also of paramount importance, since we envision
that multiple ontologies will exist in the Educational Semantic
Web.

Second, a "plug and play" interface for querying, harvesting,
contracting and delivering learning resources needs to be
established in the field and a significant penetration of this
specification is crucial. This interface shall abstract from
authentication and access control issues, whereas it also needs to
be independent from query languages and ontologies.

Third, the real user value of the all the metatagging and
interfacing needs to be demonstrated by applications such as Smart
Space for Learning, which aim at improve the effectiveness of HR
development processes. The semantic relationships of educational
artefacts with learner's needs, preferences, abilities, cultural
backgrounds and development goals need to be established and
methods for identifying them have to be studied to be able to
increase learner's satisfaction with semantic educational services.
Educational Semantic Web show cases, which prove that going beyond
the (semantic) boundaries of monolithic applications helps to
significantly improve the capabilities of learner's tools, are
considered crucial for the further evolution of the field.

Acknowledgements

This work was supported by the ELENA project
(http://www.elena-project.org/,
IST-2001-37264) and is partly sponsored by the European Commission.
Boschidar Ganev has substantially contributed to the design of the
ELENA learning service ontology. This work has benefited from the
collaborative work carried out in the ELENA project. Initiators and
discussion leaders tackling the various design issues of the
project are Barbara Kieslinger, Sigrún Gunnársdottir,
Ebba Hvannberg, Stefan Brantner and Toma? Klobučar. Monika
Frank from the Austrian Volksbanken AG served us as an inspiring
industry contact during this research.